Vehicle altitude analysis method and system based on dynamic air pressure monitoring

Abstract

The embodiment of the present invention discloses a vehicle altitude analysis method and system based on dynamic air pressure monitoring. The method includes: obtaining nine-axis sensor data, GPS sensor data and air pressure sensor data in the equipment installed on the vehicle; performing noise reduction processing on the air pressure sensor data and calculating the air pressure altitude; determining the device posture according to the nine-axis sensor data, and using the acceleration in the vertical direction to derive the vertical displacement; extracting the altitude and longitude and latitude of the GPS sensor data, and performing noise reduction processing on the altitude of the GPS sensor data; fusing the altitude of the denoised GPS sensor data with the air pressure altitude to obtain a relative altitude; combining the vertical displacement and the relative altitude to obtain the final fused altitude; integrating the final fused altitude, longitude and latitude to obtain the three-dimensional positioning data of the vehicle. By implementing the method of the embodiment of the present invention, more accurate stereo positioning information can be provided in complex urban environments.

Description

Technical Field

[0001] The present invention relates to intelligent transportation, and more specifically to a vehicle altitude analysis method and system based on dynamic air pressure monitoring. Background Art

[0002] In the current satellite positioning system, although ordinary satellite positioning can provide relatively accurate longitude and latitude information, it is easily affected by obstructions such as buildings or mountains, resulting in fluctuations in signal strength. For example, in an urban environment with dense high-rise buildings, the multipath effect is particularly serious, that is, satellite signals repeatedly reflect between buildings and reach the receiving device, causing signal delay changes and affecting positioning accuracy. In addition, electromagnetic interference between electronic devices may also lead to inaccurate positioning results.

[0003] Traditional altitude measurement relies on data from air pressure sensors. However, air pressure is affected by many factors, such as weather and temperature, which can lead to inaccurate measurement results. Especially in complex urban structures such as viaducts, this inaccuracy can significantly affect the positioning accuracy of the device and require frequent calibration. In order to maintain accuracy, air pressure sensors usually need to obtain calibration data through the Internet or rely on regular updates to the elevation database. However, these processes increase dependence on external data and increase the maintenance cost and complexity of the system.

[0004] Therefore, accurate height measurement is essential in a three-dimensional transportation network. However, the current positioning system fails to provide sufficiently accurate positioning information in complex urban environments, especially the lack of accurate height data, which may lead to incorrect position judgment during navigation or trajectory playback. At the same time, high-precision road elevation information and air pressure calibration data require additional networking services, which increases the operating risk of the system when the network signal is unstable.

[0005] Therefore, it is necessary to design a new method to provide more accurate stereo positioning information in complex urban environments. Summary of the invention

[0006] The purpose of the present invention is to overcome the defects of the prior art and provide a vehicle altitude analysis method and system based on dynamic air pressure monitoring.

[0007] To achieve the above object, the present invention adopts the following technical solution: a vehicle altitude analysis method based on dynamic air pressure monitoring, comprising:

[0008] Acquire data corresponding to the nine-axis sensor, GPS sensor, and air pressure sensor in the equipment installed on the vehicle to obtain nine-axis sensor data, GPS sensor data, and air pressure sensor data;

[0009] Perform noise reduction on the air pressure sensor data and calculate the air pressure altitude;

[0010] Determine the device attitude based on the nine-axis sensor data and use the vertical acceleration to derive the vertical displacement;

[0011] Extract altitude and longitude and latitude from GPS sensor data, and perform noise reduction on the altitude of GPS sensor data;

[0012] The height of the GPS sensor data after noise reduction is fused with the pressure height to obtain the relative height;

[0013] The final fusion height is obtained by combining the vertical displacement and relative height;

[0014] Integrate the final fused altitude, longitude and latitude to obtain the vehicle's three-dimensional positioning data.

[0015] A further technical solution is: the method of obtaining the raw data of the air pressure sensor in the device installed on the vehicle and calculating the initial air pressure altitude includes:

[0016] Obtaining raw data from an air pressure sensor in a device installed on a vehicle;

[0017] Perform average filtering on the raw data within the set time to obtain stable initial raw data of the air pressure sensor;

[0018] The initial air pressure altitude is calculated according to the stable initial air pressure sensor raw data.

[0019] A further technical solution is: the noise reduction process of the air pressure sensor data and the calculation of the air pressure altitude comprises:

[0020] Using ADF to verify the stability of the air pressure sensor data;

[0021] Determining whether the stability meets the set requirements;

[0022] If the stability does not meet the set requirements, the air pressure sensor data is subjected to differential processing using the fitted ARIMA model to obtain denoised air pressure data; wherein the fitted ARIMA model is a model formed by determining parameters through graphical analysis of autocorrelation functions and partial autocorrelation functions, and fitting the ARIMA model to the differential air pressure data sequence according to the parameters;

[0023] Calculate pressure altitude using denoised pressure data;

[0024] If the stability meets the set requirements, the barometric altitude is calculated using the barometric sensor data.

[0025] The further technical solution is: determining the device posture according to the nine-axis sensor data and using the vertical acceleration to derive the vertical displacement includes:

[0026] Transmitting the nine-axis sensor data to a heading reference system to obtain an initial device attitude;

[0027] Using an extended Kalman filter to update the initial device posture to obtain an updated device posture;

[0028] Converting the updated device posture from the device coordinate system to the world coordinate system to obtain a conversion result, wherein the conversion result includes the device posture and the acceleration in the vertical direction of the vehicle;

[0029] The vertical displacement is derived according to the acceleration in the vertical direction in the conversion result.

[0030] A further technical solution is: the vertical displacement is derived according to the acceleration in the vertical direction in the conversion result, comprising:

[0031] Integrating the acceleration in the vertical direction in the conversion result to obtain the velocity in the vertical direction;

[0032] The velocity in the vertical direction is integrated again to obtain the vertical displacement.

[0033] A further technical solution is: extracting the altitude and longitude and latitude from the GPS sensor data and performing noise reduction on the altitude of the GPS sensor data, including:

[0034] Establishing a loosely coupled strapdown inertial navigation system according to the GPS sensor data and the nine-axis sensor data to obtain longitude and latitude;

[0035] Obtaining GGA type positioning data from the GPS sensor data;

[0036] Extracting altitude information from the positioning data of the GGA type to obtain the altitude of the GPS sensor data;

[0037] The height of the GPS sensor data is subjected to noise reduction processing using an extended Kalman filter.

[0038] A further technical solution is: the altitude of the GPS sensor data after noise reduction is integrated with the air pressure altitude to obtain the relative altitude, including:

[0039] Kalman filtering is used to fuse the altitude of the denoised GPS sensor data with the barometric altitude to obtain the relative altitude.

[0040] A further technical solution is: the combination of vertical displacement and relative height to obtain the final fusion height includes:

[0041] Define the state vector and the observation vector according to the vertical displacement and relative height;

[0042] Construct a state transfer model and an observation model according to the state vector and the observation vector;

[0043] The extended Kalman filter is combined with the state transfer model and the observation model to predict the fusion height to obtain the final fusion height.

[0044] The present invention also provides a vehicle altitude analysis system based on dynamic air pressure monitoring, comprising:

[0045] A data acquisition unit, used to acquire data corresponding to a nine-axis sensor, a GPS sensor, and an air pressure sensor in a device installed on the vehicle, so as to obtain nine-axis sensor data, GPS sensor data, and air pressure sensor data;

[0046] A pressure altitude calculation unit, used to perform noise reduction processing on the pressure sensor data and calculate the pressure altitude;

[0047] A vertical displacement calculation unit, used to determine the device attitude based on the nine-axis sensor data and derive the vertical displacement using the acceleration in the vertical direction;

[0048] An altitude determination unit, used to extract altitude and longitude and latitude from GPS sensor data and perform noise reduction on the altitude of the GPS sensor data;

[0049] The first fusion unit is used to fuse the altitude of the GPS sensor data after noise reduction with the air pressure altitude to obtain a relative altitude;

[0050] The second fusion unit is used to combine the vertical displacement and the relative height to obtain the final fusion height;

[0051] The integration unit is used to integrate the final fused altitude, longitude and latitude to obtain the three-dimensional positioning data of the vehicle.

[0052] Its further technical solution is: the vertical displacement calculation unit includes:

[0053] An initial attitude determination subunit is used to transmit the nine-axis sensor data to the heading reference system to obtain the initial device attitude; an attitude update subunit is used to update the initial device attitude using an extended Kalman filter to obtain an updated device attitude; a coordinate conversion subunit is used to convert the updated device attitude from the device coordinate system to the world coordinate system to obtain a conversion result; a displacement derivation subunit is used to derive the vertical displacement according to the acceleration in the vertical direction in the conversion result.

[0054] The beneficial effects of the present invention compared with the prior art are: the present invention collects nine-axis, GPS and air pressure sensor data, performs noise reduction processing to calculate the air pressure altitude and derive the vertical displacement; finally, by fusing the GPS and air pressure altitude data and combining the vertical displacement, accurate vehicle three-dimensional positioning data is obtained, thereby providing more accurate three-dimensional positioning information in a complex urban environment.

[0055] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0057] Figure 1 A schematic diagram of a process flow of a vehicle altitude analysis method based on dynamic air pressure monitoring provided by an embodiment of the present invention;

[0058] Figure 2 A schematic diagram of a sub-process of a vehicle altitude analysis method based on dynamic air pressure monitoring provided by an embodiment of the present invention;

[0059] Figure 3 A schematic diagram of the comparison of air pressure data before and after denoising using the ARIMA model provided in an embodiment of the present invention;

[0060] Figure 4 A schematic diagram of a sub-process of a vehicle altitude analysis method based on dynamic air pressure monitoring provided by an embodiment of the present invention;

[0061] Figure 5 A schematic diagram of a sub-process of a vehicle altitude analysis method based on dynamic air pressure monitoring provided by an embodiment of the present invention;

[0062] Figure 6 A schematic diagram of a sub-process of a vehicle altitude analysis method based on dynamic air pressure monitoring provided by an embodiment of the present invention;

[0063] Figure 7 A schematic diagram of a sub-process of a vehicle altitude analysis method based on dynamic air pressure monitoring provided by an embodiment of the present invention;

[0064] Figure 8 A schematic diagram of various different heights and the height after fusion provided by an embodiment of the present invention;

[0065] Fig. 9 A schematic block diagram of a vehicle altitude analysis system based on dynamic air pressure monitoring provided by an embodiment of the present invention;

[0066] Fig.10 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0069] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0070] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0071] See also Figure 1 , Figure 1 A schematic flow chart of a vehicle altitude analysis method based on dynamic air pressure monitoring provided in an embodiment of the present invention. The vehicle altitude analysis method based on dynamic air pressure monitoring is applied in a server. The server interacts with a device installed on the vehicle, which includes an air pressure sensor, a nine-axis sensor, and a GPS sensor; the server uses the vehicle's vertical acceleration, geographic location coordinates, and comprehensive data of the device's real-time altitude to accurately calculate the device's position in three-dimensional space, and can operate independently without relying on external data sources for calibration. Through the fusion calculation of internal sensor data, it can effectively suppress the multipath effect caused by reflected signals, especially providing high-precision three-dimensional positioning information in complex urban environments. The inertial navigation algorithm can maintain accurate positioning capabilities in the absence of GPS signals or when the signals are blocked, thereby improving operational efficiency and reliability in various challenging environments.

[0072] Figure 1FIG. 1 is a flow chart of a vehicle altitude analysis method based on dynamic air pressure monitoring provided by an embodiment of the present invention. Figure 1 As shown, the method includes the following steps S110 to S170.

[0073] S110, acquiring data corresponding to the nine-axis sensor, the GPS sensor, and the air pressure sensor in the equipment installed on the vehicle to obtain the nine-axis sensor data, the GPS sensor data, and the air pressure sensor data.

[0074] In this embodiment, the nine-axis sensor data includes accelerometer data, gyroscope data, and magnetometer data, wherein the accelerometer data refers to the acceleration of the vehicle in three axes, which is used to determine the acceleration and deceleration of the vehicle. The gyroscope data refers to the angular velocity of the vehicle, that is, the speed at which the vehicle rotates around the three axes, which is used to determine the steering and attitude of the vehicle. The magnetometer data refers to the strength and direction of the surrounding magnetic field, which can be used to determine the orientation of the vehicle and attitude adjustment during navigation.

[0075] The combination of nine-axis sensor data can provide precise attitude and orientation information of the vehicle, which helps with accurate navigation and positioning.

[0076] The GPS sensor data includes latitude and longitude information and speed information, wherein the latitude and longitude information is used to provide the current geographic location coordinates of the vehicle; the speed information is used to provide the current speed of the vehicle.

[0077] The barometric pressure sensor data includes atmospheric pressure information, which is used to measure the atmospheric pressure at the current vehicle location. The vehicle's altitude can be calculated based on atmospheric pressure and temperature information for altitude-related applications such as altimeters and meteorological measurements.

[0078] In another embodiment, before step S110, the method further includes:

[0079] Get the raw data of the air pressure sensor in the device installed on the vehicle and calculate the initial air pressure altitude.

[0080] In this embodiment, the initial air pressure altitude refers to the altitude value calculated from the original value of the air pressure sensor collected by the vehicle when the device is just started.

[0081] In one embodiment, the step may include the steps of:

[0082] Get the raw data of the air pressure sensor in the device installed on the vehicle.

[0083] In this embodiment, when the device is just started, the raw data measured by the air pressure sensor installed on the vehicle device is obtained. The air pressure sensor can measure the pressure of the surrounding atmosphere, and this data is the basis for subsequent altitude calculation. The atmospheric pressure data of the current vehicle location can be obtained in real time; the air pressure sensor data does not rely on satellite signals, so it can still provide a reliable basis for altitude calculation even when the GPS signal is unstable or blocked.

[0084] The raw data within the set time is averaged and filtered to obtain stable initial raw data of the air pressure sensor.

[0085] In this embodiment, the stable initial raw data of the air pressure sensor refers to the air pressure value obtained after filtering the obtained raw data of the air pressure.

[0086] Specifically, due to the physical characteristics of the sensor, the detected data may jump when it is just started. In order to prevent the initial raw data from being affected by the data jump, the raw data within 10 seconds is averaged and filtered.

[0087] The raw data obtained from the pressure sensor is processed by average filtering to reduce possible noise and sudden fluctuations, and obtain more stable and accurate initial pressure sensor raw data. Average filtering can effectively reduce transient noise in sensor data, making subsequent altitude calculation more stable and reliable. The average value obtained is closer to the actual atmospheric pressure, which helps to accurately calculate the altitude.

[0088] The initial air pressure altitude is calculated according to the stable initial air pressure sensor raw data.

[0089] In this embodiment, the initial altitude of the vehicle, that is, the initial pressure altitude, is calculated using the provided altitude calculation formula according to the stable initial pressure sensor raw data after the average value filtering process.

[0090] The formula for calculating pressure altitude is: ;in, is the standard atmospheric pressure (101.325 kPa); P is the actually measured atmospheric pressure, in kPa; T is the actually measured temperature, in °C; h is the measured altitude, in m.

[0091] In this embodiment, the air pressure sensor of the device carries a temperature sensor, which can test the temperature while measuring the atmospheric pressure, so as to calculate the air pressure altitude.

[0092] Calculations are performed using internal sensor data without relying on external data sources or network connections, which enhances the independence and reliability of the system. Based on real-time measurements of atmospheric pressure and temperature, the calculated altitude can provide accurate vehicle position information, which is particularly useful for application scenarios that require high-precision stereo positioning.

[0093] In addition, the GPS sensor will also obtain altitude data when the device is started, and calculate the altitude after filtering and noise reduction.

[0094] Through the above steps, the system can quickly obtain and calculate the vehicle's initial altitude when the vehicle starts, providing important basic data for subsequent positioning.

[0095] S120: Perform noise reduction processing on the air pressure sensor data and calculate the air pressure altitude.

[0096] In one embodiment, see Figure 2 The above-mentioned step S120 may include steps S121~S125.

[0097] S121. Use ADF to verify the stability of the air pressure sensor data.

[0098] In this embodiment, the stationarity of the air pressure data is determined, which is a prerequisite for many time series analysis methods, including the ARIMA model. Only stationary data can be used to apply the ARIMA model for prediction and noise reduction; stationary data is easier to process and predict, which can improve the accuracy of subsequent altitude calculations.

[0099] S122: Determine whether the stability meets the set requirements.

[0100] In this embodiment, according to the result of the ADF test, if the data is not stationary, differential processing needs to be performed until stationarity is achieved.

[0101] S123. If the stationarity does not meet the set requirements, the air pressure sensor data is subjected to differential processing using the fitted ARIMA model to obtain denoised air pressure data; wherein the fitted ARIMA model is a model formed by determining parameters through graphical analysis of autocorrelation functions and partial autocorrelation functions, and fitting the ARIMA model to the differential air pressure data sequence according to the parameters.

[0102] In this embodiment, the graphical analysis of the autocorrelation function (ACF) and the partial autocorrelation function (PACF) is used to determine the optimal ARIMA model parameters, wherein the ARIMA model parameters include p, q, and d, where p represents the number of autoregressive terms, q represents the number of moving average terms, and d represents the difference order; according to the determined p, q, and d parameters, the ARIMA model is fitted to the differential air pressure data sequence; the fitted ARIMA model is used to predict the future air pressure data, thereby reducing the noise of the current air pressure data.

[0103] Also, see Figure 3 , Figure 3 The graphs of the pressure data before and after denoising are presented. It can be seen that the ARIMA model can achieve denoising by eliminating random noise and extracting trend information from the pressure data; the denoised pressure data is more suitable for calculating pressure altitude and reducing the error caused by noise. The accuracy of altitude calculation is significantly improved, especially in complex and noisy meteorological conditions.

[0104] S124. Calculate the pressure altitude using the denoised pressure data.

[0105] In this embodiment, the Calculate pressure altitude.

[0106] The pressure altitude calculated using the processed data is more accurate, effectively removing the noise and random fluctuations in the raw data.

[0107] S125: If the stability meets the set requirements, the air pressure altitude is calculated using the air pressure sensor data.

[0108] Of course, if the acquired air pressure sensor data is stable enough, you can directly use Calculate pressure altitude.

[0109] S130 . Determine the device posture according to the nine-axis sensor data, and use the acceleration in the vertical direction to derive the vertical displacement.

[0110] In one embodiment, see Figure 4 The above-mentioned step S130 may include steps S131~S134.

[0111] S131, transmitting the nine-axis sensor data to a heading reference system to obtain an initial device attitude.

[0112] In this embodiment, the initial attitude of the device can be obtained by processing the gyroscope, accelerometer and magnetometer data of the nine-axis sensor through the attitude reference system. This is crucial for subsequent coordinate system conversion and data processing to ensure the accuracy and stability of subsequent calculations.

[0113] S132: Update the initial device posture using an extended Kalman filter to obtain an updated device posture.

[0114] In this embodiment, the data from the nine-axis sensor is nonlinear, and the extended Kalman filter can effectively fuse the nonlinear data from the nine-axis sensor with the attitude data obtained by the heading reference system to improve the accuracy and stability of the attitude data. This is crucial for the coordinate system conversion and position calculation in the subsequent steps.

[0115] The extended Kalman filter is an extended form of the Kalman filter, which is used to deal with the state estimation problem of nonlinear systems. Since the optimal characteristics of the Kalman filter do not hold in nonlinear systems, and the data in the strapdown system present typical nonlinear characteristics, the classical Kalman filter cannot be used directly, and the extended Kalman filter is needed to deal with these situations; the extended Kalman filter approximates the nonlinear part of the system by using the first-order Taylor expansion in the nonlinear system, so that the state estimation method of the Kalman filter can be applied to a wider range of system types.

[0116] S133 . Convert the updated device posture from the device coordinate system to the world coordinate system to obtain a conversion result.

[0117] In this embodiment, the conversion result refers to the result obtained by converting the data corresponding to the updated device posture from the device coordinate system to the world coordinate system, wherein the conversion result includes the device posture and the acceleration in the vertical direction of the vehicle.

[0118] In this embodiment, when the device is installed on different vehicles or at different locations, the data in the device coordinate system needs to be converted into a unified world coordinate system to ensure the consistency and comparability of the position and posture data. This can avoid data errors caused by changes in the installation position or angle.

[0119] S134. Derive the vertical displacement according to the acceleration in the vertical direction in the conversion result.

[0120] In this embodiment, the displacement change of the vehicle in the vertical direction refers to the distance the vehicle rises or falls along the direction of gravity (usually the vertical axis of the vehicle).

[0121] Since the change of air pressure sensor data will be affected by environmental factors such as weather changes, relying solely on air pressure sensor data may lead to inaccurate height calculation, so vertical acceleration data is introduced to compensate and correct the height information. When using vertical acceleration data to compensate and correct the height information calculated based on the air pressure sensor data, considering that the device may be installed on different types of vehicles, and the installation position and angle may be different, obtaining vertical acceleration data requires multi-step coordinate system conversion. First, the data captured by the sensor is in the sensor coordinate system and needs to be converted to the device coordinate system to ensure that the acceleration data is consistent with the direction of movement of the device. Next, the device posture obtained by the nine-axis sensor is used to obtain the quaternion or Euler angle through the posture algorithm, and then the device coordinate system is converted to the world coordinate system through the transfer matrix to form a conversion result. Then, the vertical displacement is derived based on the vertical acceleration in the conversion result. The conversion process ensures that the acquired acceleration data is consistent with the motion state of the specific vehicle and is not affected by the installation position and vehicle type, thereby ensuring the accuracy and reliability of the data.

[0122] In one embodiment, see Figure 5 , the above-mentioned step S134 may include steps S1341~S1342.

[0123] S1341. Integrate the acceleration in the vertical direction in the conversion result to obtain the velocity in the vertical direction.

[0124] In this embodiment, the vertical displacement is calculated using the vertical acceleration data of the vehicle, and then converted into a height change. The acceleration integration obtains the vertical velocity of the vehicle. .in, is the vertical acceleration at time t; is the velocity in the vertical direction.

[0125] S1342. Integrate the velocity in the vertical direction again to obtain the vertical displacement.

[0126] In this embodiment, the vertical velocity is obtained. Integrating gives the displacement: ,in, is the vertical displacement obtained by integrating the acceleration.

[0127] S140: extracting the altitude and longitude and latitude from the GPS sensor data, and performing noise reduction processing on the altitude of the GPS sensor data.

[0128] In this embodiment, since a bumpy road may affect the accuracy of acceleration, it is necessary to add the altitude data in the GPS data for auxiliary judgment to ensure the accuracy of the final altitude.

[0129] In one embodiment, see Figure 6 , the above-mentioned step S140 may include steps S141~S144.

[0130] S141. Establish a loosely coupled strapdown inertial navigation system according to the GPS sensor data and the nine-axis sensor data to obtain longitude and latitude.

[0131] In this embodiment, the longitude and latitude refer to the longitude and latitude of the vehicle.

[0132] By combining the data collected by the positioning sensor and the nine-axis sensor, a loosely coupled strapdown inertial navigation system can be established to obtain high-precision longitude and latitude positioning data. In this system, the GPS sensor and the nine-axis sensor operate independently, and the Kalman filter is used for data fusion, which is flexible and reliable.

[0133] Specifically, the GPS sensor is responsible for obtaining the location information of the global positioning system, while the nine-axis sensor provides local motion state data such as the device's attitude and acceleration. Through Kalman filtering, these two types of data are effectively integrated, which not only improves the accuracy of positioning, but also can correct GPS positioning errors caused by signal shielding or multipath effects.

[0134] S142: Acquire GGA type positioning data from the GPS sensor data.

[0135] In this embodiment, GGA (Global Positioning System FixData) data is a standard data format output by a GPS (Global Positioning System) receiver, which contains detailed data about GPS positioning and time information. Specifically, it includes the following information:

[0136] UTC time: The UTC time of the current location, expressed in hours, minutes and seconds.

[0137] Latitude and Longitude: The latitude and longitude of your current location, usually expressed in degrees, minutes, seconds format (or decimal degrees format).

[0138] Fix Quality Indicator: Indicates the quality of GPS positioning, such as the type of positioning (invalid, single point positioning, differential positioning, etc.).

[0139] Number of satellites: The number of GPS satellites used to calculate your current position.

[0140] HDOP (Horizontal Dilution of Precision): Horizontal Dilution of Precision, used to evaluate the accuracy of GPS signals.

[0141] Altitude: Height relative to sea level, in meters.

[0142] Geoid Separation: The height difference between the Earth's ellipsoid and mean sea level.

[0143] Differential GPS Data: Provides differential GPS correction data if available.

[0144] S143: Extract altitude information from the GGA type positioning data to obtain the altitude of the GPS sensor data.

[0145] In this embodiment, the altitude information can be directly extracted from the GGA type positioning data to form the altitude of the GPS sensor data.

[0146] S144, performing noise reduction processing on the altitude of the GPS sensor data using an extended Kalman filter.

[0147] In this embodiment, the altitude information is extracted using the GGA type positioning data in the GPS sensor, and is processed by an extended Kalman filter to obtain the height data after noise reduction. This process not only improves altitude accuracy, but also enhances sensitivity to ground changes and altitude variations, particularly useful in applications in dynamic environments.

[0148] For step S150, a loosely coupled strapdown inertial navigation system is used, combining the advantages of GPS sensors and nine-axis sensors, and data fusion is achieved through Kalman filtering technology. It can not only provide accurate longitude and latitude positioning, but also improve the accuracy and reliability of altitude measurement, and is suitable for various complex navigation and positioning needs.

[0149] S150, fusing the altitude of the GPS sensor data after noise reduction with the atmospheric pressure altitude to obtain a relative altitude.

[0150] In this embodiment, the relative height refers to a height change value of an object relative to a reference surface obtained by fusing the height of the GPS sensor data after noise reduction with the air pressure height.

[0151] Specifically, Kalman filtering is used to fuse the altitude and barometric altitude of the denoised GPS sensor data to obtain the relative altitude.

[0152] In multi-sensor fusion applications, in order to improve the accuracy of calculating altitude, Kalman Filter is usually used to fuse data from different sensors, such as GPS sensor altitude and pressure altitude. Through Kalman filtering, these two types of measurement data can be effectively combined to obtain a more accurate relative altitude. .

[0153] Specifically, Kalman filtering can effectively utilize the information of multiple sensors by dynamically combining the measurement data of different sensors and their error characteristics, and dynamically adjust the data weights according to their measurement accuracy and reliability, thereby improving the accuracy of the final calculation results; by considering the random noise and systematic errors in sensor measurements, Kalman filtering can suppress these noises during the data fusion process and reduce the height calculation deviation caused by measurement errors; moreover, Kalman filtering is a recursive algorithm that can update the estimated value in real time when receiving new measurement data, so it is very suitable for real-time applications.

[0154] Therefore, by fusing the altitude and pressure altitude of GPS sensor data through Kalman filtering, the accuracy of altitude calculation can be improved while making full use of the advantages of different sensors, thereby ensuring that reliable and accurate relative altitude information can be obtained under various environmental conditions.

[0155] S160. Combine the vertical displacement and the relative height to obtain the final fusion height.

[0156] In this embodiment, the final fusion altitude refers to the altitude obtained after multi-sensor fusion processing, which is an altitude estimation with higher accuracy and reliability.

[0157] In one embodiment, see Figure 7 The above-mentioned step S160 may include steps S161~S163.

[0158] S161. Define a state vector and an observation vector according to the vertical displacement and the relative height.

[0159] In this embodiment, the state vector Contains system state parameters, such as position and speed. In this embodiment, it can include relative height and other state variables, such as velocity; the observation vector Contains observations obtained from sensor measurements, such as altitude data measured by GPS and air pressure sensors.

[0160] S162. Construct a state transition model and an observation model according to the state vector and the observation vector.

[0161] In this embodiment, the state transition model describes how the state of the system evolves between time t and t+1. This can be defined based on physical equations or a dynamic model of the system. The observation model maps the state vector to the observation vector space, that is, describes how the sensor measures the various parameters of the state vector.

[0162] S163. Use the extended Kalman filter in combination with the state transfer model and the observation model to predict the fusion height to obtain the final fusion height.

[0163] In this embodiment, the state transition model is used to estimate the state of the previous moment. , predict the current state, taking into account the dynamic characteristics and noise of the system during the prediction process. , using the observation model and current sensor measurements, combined with the predicted state, the optimal state estimate is calculated using the Kalman gain. This step compares the predicted state of the system with the actual observations and adjusts the estimate to reduce the error.

[0164] The above steps use the extended Kalman filter to optimize the state estimate by dynamically combining the predicted and actual observation data, thereby improving the measurement accuracy of height and displacement; it enables the state estimate to be updated instantly as new observation data is continuously received; the extended Kalman filter can handle nonlinear systems and non-Gaussian distributed noise, making it suitable for complex real-world applications.

[0165] By establishing and applying the extended Kalman filter, we can effectively process the height data of multi-sensor fusion, improve the measurement accuracy and system stability, and adapt to various complex environments and dynamic changes.

[0166] The relationship between the altitude converted from the pressure sensor, the altitude of the GPS sensor data, the vertical displacement, the relative altitude obtained by fusion, and the final fusion altitude over time is as follows: Figure 8As shown. It can be seen from the figure that the data change frequency of the GPS sensor data height, air pressure sensor height, and vertical displacement height corresponding to each moment is relatively high. Because the GPS sensor data is easily affected by the signal strength, and the barometer data is easily affected by the environment such as the temperature in the car, the air pressure sensor data is fused with the GPS sensor data to obtain the relative height. Then, the final fused height is obtained by fusing the vertical displacement with the relative height. The change in vertical displacement distance may be affected by the vehicle's driving state and road conditions. Therefore, the fused height is obtained by filtering the relative height and vertical displacement, which can effectively reduce the influence of sensor noise and sudden interference. Specifically, the filter can eliminate drastic changes in a short period of time by smoothing the signal, making the data curve smoother and more coherent, thereby reflecting a more real and stable height change trend. This processing method is particularly important for GPS sensors and air pressure sensors because their raw data are easily affected by environmental changes and instantaneous interference.

[0167] S170: Integrate the final fused altitude, longitude and latitude to obtain three-dimensional positioning data of the vehicle.

[0168] In this embodiment, the three-dimensional positioning data of the vehicle refers to data including the final fused height, longitude and latitude, and the final fused height, longitude and latitude are combined into a set to represent the three-dimensional positioning data of the vehicle.

[0169] The final fusion height curve is smooth and continuous, showing strong stability and accuracy. This shows that after comprehensively considering multiple sensor data, the final fusion height can effectively reduce the impact of single sensor errors. The above-mentioned vehicle altitude analysis method based on dynamic air pressure monitoring obtains the air pressure sensor data installed on the vehicle, calculates the initial air pressure height, collects nine-axis, GPS and air pressure sensor data, and performs noise reduction processing to calculate the air pressure height and derive the vertical displacement; finally, by fusing the GPS and air pressure height data and combining the vertical displacement, accurate vehicle three-dimensional positioning data is obtained, so as to provide more accurate stereo positioning information in complex urban environments.

[0170] Fig. 9 FIG. 3 is a schematic block diagram of a vehicle altitude analysis system 300 based on dynamic air pressure monitoring provided by an embodiment of the present invention. Fig. 9 As shown, corresponding to the above vehicle altitude analysis method based on dynamic air pressure monitoring, the present invention also provides a vehicle altitude analysis system 300 based on dynamic air pressure monitoring. The vehicle altitude analysis system 300 based on dynamic air pressure monitoring includes a unit for executing the above vehicle altitude analysis method based on dynamic air pressure monitoring, and the system can be configured in a server. Specifically, please refer to Fig. 9The vehicle altitude analysis system 300 based on dynamic air pressure monitoring includes a data acquisition unit 301, an air pressure altitude calculation unit 302, a vertical displacement calculation unit 303, an altitude determination unit 304, a first fusion unit 305, a second fusion unit 306 and an integration unit 307.

[0171] The data acquisition unit 301 is used to acquire the data corresponding to the nine-axis sensor, GPS sensor, and air pressure sensor in the equipment installed on the vehicle to obtain the nine-axis sensor data, GPS sensor data, and air pressure sensor data; the air pressure altitude calculation unit 302 is used to perform noise reduction processing on the air pressure sensor data and calculate the air pressure altitude; the vertical displacement calculation unit 303 is used to determine the device posture according to the nine-axis sensor data and derive the vertical displacement using the acceleration in the vertical direction; the altitude determination unit 304 is used to extract the altitude and longitude and latitude from the GPS sensor data and perform noise reduction processing on the altitude of the GPS sensor data; the first fusion unit 305 is used to fuse the altitude of the GPS sensor data after noise reduction with the air pressure altitude to obtain the relative altitude; the second fusion unit 306 is used to combine the vertical displacement and the relative altitude to obtain the final fused altitude; the integration unit 307 is used to integrate the final fused altitude and longitude and latitude to obtain the three-dimensional positioning data of the vehicle.

[0172] In one embodiment, the above device further includes:

[0173] an initial altitude determination unit, for acquiring raw data from a barometric pressure sensor in a device installed on the vehicle, and calculating an initial barometric pressure altitude;

[0174] The initial height determination unit comprises:

[0175] The raw data acquisition subunit is used to acquire the raw data of the air pressure sensor in the equipment installed on the vehicle; the filtering subunit is used to perform average value filtering on the raw data within a set time to obtain stable initial raw data of the air pressure sensor; the initial altitude calculation subunit is used to calculate the initial air pressure altitude based on the stable initial raw data of the air pressure sensor.

[0176] In one embodiment, the pressure altitude calculation unit 302 includes:

[0177] A verification subunit is used to use ADF to verify the stability of the air pressure sensor data; a judgment subunit is used to judge whether the stability meets the set requirements; a differential processing subunit is used to use the fitted ARIMA model to perform differential processing on the air pressure sensor data if the stability does not meet the set requirements to obtain denoised air pressure data; wherein the fitted ARIMA model is a model formed by determining parameters through graphical analysis of autocorrelation functions and partial autocorrelation functions, and fitting the ARIMA model to the differential air pressure data sequence according to the parameters; a first calculation subunit is used to calculate the air pressure altitude using the denoised air pressure data; a second calculation subunit is used to calculate the air pressure altitude using the air pressure sensor data if the stability meets the set requirements.

[0178] In one embodiment, the vertical displacement calculation unit 303 includes:

[0179] An initial attitude determination subunit is used to transmit the nine-axis sensor data to the heading reference system to obtain an initial device attitude; an attitude update subunit is used to update the initial device attitude using an extended Kalman filter to obtain an updated device attitude; a coordinate conversion subunit is used to convert the updated device attitude from the device coordinate system to the world coordinate system to obtain a conversion result; wherein the conversion result includes the device attitude and the acceleration in the vertical direction of the vehicle; a displacement derivation subunit is used to derive the vertical displacement according to the acceleration in the vertical direction in the conversion result.

[0180] In one embodiment, the displacement derivation subunit comprises:

[0181] The first integration module is used to integrate the acceleration in the vertical direction in the conversion result to obtain the velocity in the vertical direction; the second integration module is used to integrate the velocity in the vertical direction again to obtain the vertical displacement.

[0182] In one embodiment, the height determination unit 304 includes:

[0183] A latitude and longitude determination subunit is used to establish a loosely coupled strapdown inertial navigation system according to the GPS sensor data and the nine-axis sensor data to obtain longitude and latitude; a GGA type positioning data acquisition subunit is used to obtain GGA type positioning data from the GPS sensor data; a GPS sensor data altitude extraction subunit is used to extract altitude information from the GGA type positioning data to obtain the altitude of the GPS sensor data; a noise reduction processing subunit is used to perform noise reduction processing on the altitude of the GPS sensor data using an extended Kalman filter.

[0184] In one embodiment, the first fusion unit 305 is used to use Kalman filtering to fuse the altitude and the barometric altitude of the GPS sensor data after noise reduction to obtain the relative altitude.

[0185] In one embodiment, the second fusion unit 306 includes:

[0186] The vector definition subunit is used to define the state vector and the observation vector according to the vertical displacement and the relative height. The model construction subunit is used to construct the state transfer model and the observation model according to the state vector and the observation vector. The prediction subunit is used to use the extended Kalman filter in combination with the state transfer model and the observation model to predict the fusion height to obtain the final fusion height.

[0187] It should be noted that technicians in the relevant field can clearly understand that the specific implementation process of the above-mentioned vehicle altitude analysis system 300 based on dynamic air pressure monitoring and each unit can refer to the corresponding description in the aforementioned method embodiment, and for the convenience and conciseness of the description, it will not be repeated here.

[0188] The vehicle altitude analysis system 300 based on dynamic air pressure monitoring can be implemented in the form of a computer program. Figure 4 Runs on the computer device shown.

[0189] See also Fig.10 , Fig.10 5 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 may be a server, wherein the server may be an independent server or a server cluster composed of multiple servers.

[0190] See also Fig.10 The computer device 500 includes a processor 502 , a memory and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0191] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, and when the program instructions are executed, the processor 502 can execute a vehicle altitude analysis method based on dynamic air pressure monitoring.

[0192] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

[0193] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a vehicle altitude analysis method based on dynamic air pressure monitoring.

[0194] The network interface 505 is used to communicate with other devices over the network. Fig.10 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0195] The processor 502 is used to run the computer program 5032 stored in the memory to implement the following steps:

[0196] Acquire the data corresponding to the nine-axis sensor, GPS sensor, and air pressure sensor in the equipment installed on the vehicle to obtain the nine-axis sensor data, GPS sensor data, and air pressure sensor data; perform noise reduction processing on the air pressure sensor data to calculate the air pressure altitude; determine the device posture according to the nine-axis sensor data, and use the vertical acceleration to derive the vertical displacement; extract the altitude and longitude and latitude from the GPS sensor data, and perform noise reduction processing on the altitude of the GPS sensor data; fuse the altitude of the denoised GPS sensor data with the air pressure altitude to obtain the relative altitude; combine the vertical displacement and the relative altitude to obtain the final fused altitude; integrate the final fused altitude, longitude and latitude to obtain the vehicle's three-dimensional positioning data.

[0197] In one embodiment, before implementing the step of acquiring data corresponding to the nine-axis sensor, the GPS sensor, and the air pressure sensor in the device installed on the vehicle to obtain the nine-axis sensor data, the GPS sensor data, and the air pressure sensor data, the processor 502 further implements the following steps:

[0198] Get the raw data of the air pressure sensor in the device installed on the vehicle and calculate the initial air pressure altitude.

[0199] In one embodiment, the processor 502 implements the following steps when implementing the step of acquiring the raw data of the air pressure sensor in the device installed on the vehicle and calculating the initial air pressure altitude:

[0200] The raw data of the air pressure sensor in the equipment installed on the vehicle is obtained; the raw data within a set time is averaged and filtered to obtain stable initial raw data of the air pressure sensor; and the initial air pressure altitude is calculated according to the stable initial raw data of the air pressure sensor.

[0201] In one embodiment, the processor 502 implements the following steps when implementing the step of performing noise reduction processing on the air pressure sensor data and calculating the air pressure altitude:

[0202] Use ADF to verify the stationarity of the air pressure sensor data; determine whether the stationarity meets the set requirements; if the stationarity does not meet the set requirements, use the fitted ARIMA model to perform differential processing on the air pressure sensor data to obtain denoised air pressure data; wherein the fitted ARIMA model is a model formed by determining parameters through graphical analysis of autocorrelation functions and partial autocorrelation functions, and fitting the ARIMA model to the differential air pressure data sequence according to the parameters; use the denoised air pressure data to calculate the air pressure altitude; if the stationarity meets the set requirements, use the air pressure sensor data to calculate the air pressure altitude.

[0203] In one embodiment, the processor 502 implements the following steps when implementing the step of determining the device posture according to the nine-axis sensor data and deriving the vertical displacement using the acceleration in the vertical direction:

[0204] The nine-axis sensor data is transmitted to the heading reference system to obtain an initial device attitude; the initial device attitude is updated using an extended Kalman filter to obtain an updated device attitude; the updated device attitude is converted from the device coordinate system to the world coordinate system to obtain a conversion result; wherein the conversion result includes the device attitude and the acceleration in the vertical direction of the vehicle; and the vertical displacement is derived according to the acceleration in the vertical direction in the conversion result.

[0205] In one embodiment, when the processor 502 implements the step of deriving the vertical displacement according to the acceleration in the vertical direction in the conversion result, the processor 502 specifically implements the following steps:

[0206] The acceleration in the vertical direction in the conversion result is integrated to obtain the velocity in the vertical direction; the velocity in the vertical direction is integrated again to obtain the vertical displacement.

[0207] In one embodiment, the processor 502 specifically implements the following steps when implementing the steps of extracting the altitude and longitude and latitude from the GPS sensor data and performing noise reduction processing on the altitude of the GPS sensor data:

[0208] A loosely coupled strapdown inertial navigation system is established according to the GPS sensor data and the nine-axis sensor data to obtain longitude and latitude; GGA type positioning data is obtained from the GPS sensor data; altitude information is extracted from the GGA type positioning data to obtain the height of the GPS sensor data; and the height of the GPS sensor data is subjected to noise reduction processing using an extended Kalman filter.

[0209] In one embodiment, when the processor 502 implements the step of fusing the altitude of the GPS sensor data after noise reduction with the air pressure altitude to obtain the relative altitude, the processor 502 specifically implements the following steps:

[0210] Kalman filtering is used to fuse the altitude of the denoised GPS sensor data with the barometric altitude to obtain the relative altitude.

[0211] In one embodiment, when the processor 502 implements the step of combining the vertical displacement and the relative height to obtain the final fusion height, the processor 502 specifically implements the following steps:

[0212] The state vector and observation vector are defined according to the vertical displacement and relative height; the state transfer model and observation model are constructed according to the state vector and observation vector; the extended Kalman filter is used in combination with the state transfer model and observation model to predict the fusion height to obtain the final fusion height.

[0213] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0214] It can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.

[0215] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein when the computer program is executed by a processor, the processor executes the following steps:

[0216] Acquire the data corresponding to the nine-axis sensor, GPS sensor, and air pressure sensor in the equipment installed on the vehicle to obtain the nine-axis sensor data, GPS sensor data, and air pressure sensor data; perform noise reduction processing on the air pressure sensor data to calculate the air pressure altitude; determine the device posture according to the nine-axis sensor data, and use the vertical acceleration to derive the vertical displacement; extract the altitude and longitude and latitude from the GPS sensor data, and perform noise reduction processing on the altitude of the GPS sensor data; fuse the altitude of the denoised GPS sensor data with the air pressure altitude to obtain the relative altitude; combine the vertical displacement and the relative altitude to obtain the final fused altitude; integrate the final fused altitude, longitude and latitude to obtain the vehicle's three-dimensional positioning data.

[0217] In one embodiment, before executing the computer program to implement the step of acquiring raw data from the air pressure sensor in the device installed on the vehicle and calculating the initial air pressure altitude, the processor further implements the following steps:

[0218] Get the raw data of the air pressure sensor in the device installed on the vehicle and calculate the initial air pressure altitude.

[0219] In one embodiment, when the processor executes the computer program to implement the step of acquiring raw data from the air pressure sensor in the device installed on the vehicle and calculating the initial air pressure altitude, the processor specifically implements the following steps:

[0220] The raw data of the air pressure sensor in the equipment installed on the vehicle is obtained; the raw data within a set time is averaged and filtered to obtain stable initial raw data of the air pressure sensor; and the initial air pressure altitude is calculated according to the stable initial raw data of the air pressure sensor.

[0221] In one embodiment, when the processor executes the computer program to implement the step of performing noise reduction processing on the air pressure sensor data and calculating the air pressure altitude, the processor specifically implements the following steps:

[0222] Use ADF to verify the stationarity of the air pressure sensor data; determine whether the stationarity meets the set requirements; if the stationarity does not meet the set requirements, use the fitted ARIMA model to perform differential processing on the air pressure sensor data to obtain denoised air pressure data; wherein the fitted ARIMA model is a model formed by determining parameters through graphical analysis of autocorrelation functions and partial autocorrelation functions, and fitting the ARIMA model to the differential air pressure data sequence according to the parameters; use the denoised air pressure data to calculate the air pressure altitude; if the stationarity meets the set requirements, use the air pressure sensor data to calculate the air pressure altitude.

[0223] In one embodiment, when the processor executes the computer program to implement the step of determining the device posture according to the nine-axis sensor data and deriving the vertical displacement using the acceleration in the vertical direction, the processor specifically implements the following steps:

[0224] The nine-axis sensor data is transmitted to the heading reference system to obtain an initial device attitude; the initial device attitude is updated using an extended Kalman filter to obtain an updated device attitude; the updated device attitude is converted from the device coordinate system to the world coordinate system to obtain a conversion result; wherein the conversion result includes the device attitude and the acceleration in the vertical direction of the vehicle; and the vertical displacement is derived according to the acceleration in the vertical direction in the conversion result.

[0225] In one embodiment, when the processor executes the computer program to implement the step of deriving the vertical displacement according to the acceleration in the vertical direction in the conversion result, the processor specifically implements the following steps:

[0226] The acceleration in the vertical direction in the conversion result is integrated to obtain the velocity in the vertical direction; the velocity in the vertical direction is integrated again to obtain the vertical displacement.

[0227] In one embodiment, when the processor executes the computer program to implement the steps of extracting the altitude and longitude and latitude from the GPS sensor data and performing noise reduction processing on the altitude of the GPS sensor data, the processor specifically implements the following steps:

[0228] A loosely coupled strapdown inertial navigation system is established according to the GPS sensor data and the nine-axis sensor data to obtain longitude and latitude; GGA type positioning data is obtained from the GPS sensor data; altitude information is extracted from the GGA type positioning data to obtain the height of the GPS sensor data; and the height of the GPS sensor data is subjected to noise reduction processing using an extended Kalman filter.

[0229] In one embodiment, when the processor executes the computer program to implement the step of fusing the altitude of the GPS sensor data after noise reduction with the air pressure altitude to obtain the relative altitude, the processor specifically implements the following steps:

[0230] Kalman filtering is used to fuse the altitude of the denoised GPS sensor data with the barometric altitude to obtain the relative altitude.

[0231] In one embodiment, when the processor executes the computer program to implement the step of combining the vertical displacement and the relative height to obtain the final fusion height, the processor specifically implements the following steps:

[0232] The state vector and observation vector are defined according to the vertical displacement and relative height; the state transfer model and observation model are constructed according to the state vector and observation vector; the extended Kalman filter is used in combination with the state transfer model and observation model to predict the fusion height to obtain the final fusion height.

[0233] The storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0234] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0235] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0236] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the system of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0237] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0238] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A vehicle altitude analysis method based on dynamic air pressure monitoring, characterized in that: include: Acquire data corresponding to the nine-axis sensor, GPS sensor, and air pressure sensor in the equipment installed on the vehicle to obtain nine-axis sensor data, GPS sensor data, and air pressure sensor data; Perform noise reduction on the air pressure sensor data and calculate the air pressure altitude; Determine the device attitude based on the nine-axis sensor data and use the vertical acceleration to derive the vertical displacement; Extract altitude and longitude and latitude from GPS sensor data, and perform noise reduction on the altitude of GPS sensor data; The height of the GPS sensor data after noise reduction is fused with the pressure height to obtain the relative height; The final fusion height is obtained by combining the vertical displacement and relative height; Integrate the final fused altitude, longitude and latitude to obtain the vehicle's three-dimensional positioning data; The combination of vertical displacement and relative height to obtain the final fusion height includes: Define the state vector and the observation vector according to the vertical displacement and relative height; Construct a state transfer model and an observation model according to the state vector and the observation vector; The extended Kalman filter is combined with the state transfer model and the observation model to predict the fusion height to obtain the final fusion height.

2. The vehicle altitude analysis method based on dynamic air pressure monitoring according to claim 1 is characterized in that: Before acquiring the data corresponding to the nine-axis sensor, the GPS sensor, and the air pressure sensor in the equipment installed on the vehicle to obtain the nine-axis sensor data, the GPS sensor data, and the air pressure sensor data, the method further includes: Obtaining raw data from a barometric pressure sensor in a device installed on a vehicle and calculating an initial barometric pressure altitude; The method of obtaining raw data of an air pressure sensor in a device installed on a vehicle and calculating an initial air pressure altitude includes: Obtaining raw data from an air pressure sensor in a device installed on a vehicle; Perform average filtering on the raw data within the set time to obtain stable initial raw data of the air pressure sensor; The initial air pressure altitude is calculated according to the stable initial air pressure sensor raw data.

3. The vehicle altitude analysis method based on dynamic air pressure monitoring according to claim 1 is characterized in that: The method of performing noise reduction processing on the air pressure sensor data and calculating the air pressure altitude includes: Using ADF to verify the stability of the air pressure sensor data; Determining whether the stability meets the set requirements; If the stability does not meet the set requirements, the air pressure sensor data is subjected to differential processing using the fitted ARIMA model to obtain denoised air pressure data; wherein the fitted ARIMA model is a model formed by determining parameters through graphical analysis of autocorrelation functions and partial autocorrelation functions, and fitting the ARIMA model to the differential air pressure data sequence according to the parameters; Calculate pressure altitude using denoised pressure data; If the stability meets the set requirements, the barometric altitude is calculated using the barometric pressure sensor data.

4. The vehicle altitude analysis method based on dynamic air pressure monitoring according to claim 1, characterized in that: Determining the device posture according to the nine-axis sensor data and using the vertical acceleration to derive the vertical displacement includes: Transmitting the nine-axis sensor data to a heading reference system to obtain an initial device attitude; Using an extended Kalman filter to update the initial device posture to obtain an updated device posture; Converting the updated device posture from the device coordinate system to the world coordinate system to obtain a conversion result, wherein the conversion result includes the device posture and the acceleration in the vertical direction of the vehicle; The vertical displacement is derived according to the acceleration in the vertical direction in the conversion result.

5. The vehicle altitude analysis method based on dynamic air pressure monitoring according to claim 4 is characterized in that: The derivation of the vertical displacement according to the acceleration in the vertical direction in the conversion result comprises: Integrating the acceleration in the vertical direction in the conversion result to obtain the velocity in the vertical direction; The velocity in the vertical direction is integrated again to obtain the vertical displacement.

6. The vehicle altitude analysis method based on dynamic air pressure monitoring according to claim 1, characterized in that: The step of extracting the altitude and longitude and latitude of the GPS sensor data from the GPS sensor data and performing noise reduction processing on the altitude of the GPS sensor data includes: Establishing a loosely coupled strapdown inertial navigation system according to the GPS sensor data and the nine-axis sensor data to obtain longitude and latitude; Obtaining GGA type positioning data from the GPS sensor data; Extracting altitude information from the positioning data of the GGA type to obtain the altitude of the GPS sensor data; The height of the GPS sensor data is subjected to noise reduction processing using an extended Kalman filter.

7. The vehicle altitude analysis method based on dynamic air pressure monitoring according to claim 1, characterized in that: The method of fusing the altitude of the GPS sensor data after noise reduction with the air pressure altitude to obtain the relative altitude includes: Kalman filtering is used to fuse the altitude of the denoised GPS sensor data with the barometric altitude to obtain the relative altitude.

8. A vehicle altitude analysis system based on dynamic air pressure monitoring, characterized in that: include: an initial altitude determination unit, for acquiring raw data from a barometric pressure sensor in a device installed on the vehicle, and calculating an initial barometric pressure altitude; A data acquisition unit, used to acquire data corresponding to a nine-axis sensor, a GPS sensor, and an air pressure sensor in a device installed on the vehicle, so as to obtain nine-axis sensor data, GPS sensor data, and air pressure sensor data; A pressure altitude calculation unit, used to perform noise reduction processing on the pressure sensor data and calculate the pressure altitude; A vertical displacement calculation unit, used to determine the device attitude based on the nine-axis sensor data and derive the vertical displacement using the acceleration in the vertical direction; An altitude determination unit, used to extract altitude and longitude and latitude from GPS sensor data and perform noise reduction on the altitude of the GPS sensor data; The first fusion unit is used to fuse the altitude of the GPS sensor data after noise reduction with the air pressure altitude to obtain a relative altitude; The second fusion unit is used to combine the vertical displacement and the relative height to obtain the final fusion height; An integration unit is used to integrate the final fused altitude, longitude and latitude to obtain the three-dimensional positioning data of the vehicle; The second fusion unit comprises: A vector definition subunit, used to define a state vector and an observation vector according to a vertical displacement and a relative height; The model building subunit is used to build a state transfer model and an observation model based on the state vector and the observation vector; the prediction subunit is used to use the extended Kalman filter in combination with the state transfer model and the observation model to predict the fusion height to obtain the final fusion height.

9. The vehicle altitude analysis system based on dynamic air pressure monitoring according to claim 8, characterized in that: The vertical displacement calculation unit comprises: An initial attitude determination subunit is used to transmit the nine-axis sensor data to the heading reference system to obtain the initial device attitude; an attitude update subunit is used to update the initial device attitude using an extended Kalman filter to obtain an updated device attitude; a coordinate conversion subunit is used to convert the updated device attitude from the device coordinate system to the world coordinate system to obtain a conversion result; a displacement derivation subunit is used to derive the vertical displacement according to the acceleration in the vertical direction in the conversion result.

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